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Record W4410158303 · doi:10.3167/hrrh.2025.510206

Visualizing Elizabeth of York's Ladies-in-Waiting

2025· article· en· W4410158303 on OpenAlexvenueno aff
Caroline Dunn, M. E. Bailey

Bibliographic record

VenueHistorical Reflections/Réflexions Historiques · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArtSociologyArt history

Abstract

fetched live from OpenAlex

Abstract Medieval ladies-in-waiting were sophisticated players who earned significant rewards and engaged in power politics. This article quantifies and visualizes how female attendants accessed royal power and influence during the reign of Elizabeth of York (d. 1503), consort of Henry VII (r. 1485–1509). Using prosopographical techniques to build a collective biography of Elizabeth's highborn servants, analyzed with three related Microsoft Access databases, the investigation uncovered over 350 references to their activities. Employing Net.Create software and techniques of network analysis, this study contextualizes both their duties and their rewards. Visually representing proximity to the monarch reveals, beyond anecdotes, how rulers relied upon some ladies-in-waiting more than others. Informed by gender studies, this article supports recent queen-focused analyses of female power that have sought to challenge the long-standing narrative that male-dominated monarchy excluded female participation and access to power by revealing the prominence and activities of female courtiers in late medieval England.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.318
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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